This thesis examines the extent of agroforestry in Michigan, Ohio, and Wisconsin by combining high-resolution spatial analysis of linear woody features with landowner survey data. The primary aim is to document the prevalence of practices such as windbreaks and riparian forest buffers and to investigate the management intentions explaining their genesis. Convolutional neural networks (CNNs) were employed to create a sub-meter land cover product using US Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) imagery, and shape-based metrics were then used to detect the presence of linear small woody features. Validation and case studies in 35 counties indicate that this approach accurately pinpoints narrow tree lines in agricultural landscapes.Parallel survey work engaged landowners through a multi-wave mailing strategy. Participants described their use of woody features, offering details on motivations, management intensity, and plans for future tree establishment or maintenance. Results demonstrated alignment between survey-reported windbreaks and riparian forest buffers and the automated mapping outputs in many cases, though some discrepancies arose in parcels with fragmented ownership or minimal maintenance. The findings emphasize the significance of precise, high-resolution classification methods for quantifying agroforestry practices at scale. They also highlight how social and economic factors shape whether landowners consider these woody features essential to farm and forested systems. By integrating spatial and survey-based evidence, this thesis provides a fuller perspective on agroforestry extent and adoption in the Lake States and presents strategies to refine classification thresholds. The multi-layered methodology can inform regional policymakers, resource managers, and extension services seeking to recognize and support these beneficial tree-based practices.
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